A semi-supervised point-cloud reconstruction network with category-specific fused shape priors and a self-attention decoder improves single-view 3D reconstruction under 1-20% labeled data, though by small margins.
Reconfusion: 3d reconstruction with di ffusion priors
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Semi-supervised Single-view 3D Reconstruction via Multi Shape Prior Fusion Strategy and Self-Attention
A semi-supervised point-cloud reconstruction network with category-specific fused shape priors and a self-attention decoder improves single-view 3D reconstruction under 1-20% labeled data, though by small margins.